Evaluation and risk identification of diabetic foot ulcers using SVM algorithm
Author(s):
ISHWARYA S, JANAKI R, GOWSALYA R, Dr. A. Sumaiya Begum
Keywords:
Diabetic foot ulcers, machine learning, classification, gangrene
Abstract
Diabetes Mellitus(DM) often called as Diabetes, is a lifelong disease which affects the human body due to damaged pancreas producing inadequate aggregate of insulin. One of the major problems in diabetic patients is the development of diabetic foot ulcers (DFU). This leads to gangrene formation and loss of sensation in the patient’s foot. If ignored, it may lead to lower leg amputation. The presently available clinical techniques to DFU treatment depend on patient and clinical surveillance which has more significant limitations such as the high cost involved in the diagnosis, treatment and lengthy care of the diabetic foot ulcer. A large dataset of foot images which contain DFU from diabetic patients has been collected. We have used SVM (Support Vector Machine) algorithm for classification. This experiment is performed to evaluate the skin conditions of medium level(low risk) and abnormal level(high risk). The present work defines evaluation and risk identification of the level of diabetic foot ulcers whether it is medium or abnormal in display through microcontroller and the message will be sent to the concerned person using GSM (Global System for Mobile communication).
Article Details
Unique Paper ID: 149574

Publication Volume & Issue: Volume 6, Issue 12

Page(s): 906 - 909
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